frbs v3.1-0


Monthly downloads



Fuzzy Rule-Based Systems for Classification and Regression Tasks

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

Functions in frbs

Name Description
FRBCS.eng FRBCS: prediction phase
frbsPMML The frbsPMML generator
rulebase The rule checking function
DM.update FIR.DM updating function
ANFIS ANFIS model building
FRBCS.CHI FRBCS.CHI model building
GFS.GCCL GFS.GCCL model building The data de-normalization
GFS.LT.RS.test GFS.LT.RS: The prediction phase
summary.frbs The summary function for frbs objects
FIR.DM FIR.DM model building
frbs.eng The prediction phase
HGD.update FS.HGD updating function
ANFIS.update ANFIS updating function
defuzzifier Defuzzifier to transform from linguistic terms to crisp values
data.gen3d A data generator
predict.frbs The frbs prediction stage
GFS.GCCL.eng GFS.GCCL.test: The prediction phase
ECM Evolving Clustering Method
FS.HGD FS.HGD model building
GFS.Thrift GFS.Thrift model building
GFS.FR.MOGUL GFS.FR.MOGUL model building
frbs-package Getting started with the frbs package
FRBCS.W FRBCS.W model building
FH.GBML FH.GBML model building
WM WM model building
GFS.LT.RS GFS.LT.RS model building
SLAVE.test SLAVE.test: The prediction phase
inference The process of fuzzy reasoning
GFS.FR.MOGUL.test GFS.FR.MOGUL: The prediction phase
SBC The subtractive clustering and fuzzy c-means (SBC) model building
read.frbsPMML The frbsPMML reader
frbsObjectFactory The object factory for frbs objects
fuzzifier Transforming from crisp set into linguistic terms
SLAVE SLAVE model building
frbsData Data set of the package
frbs.gen The frbs model generator
DENFIS DENFIS model building
plotMF The plotting function The data normalization
HyFIS HyFIS model building
SBC.test SBC prediction phase
GFS.Thrift.test GFS.Thrift: The prediction phase
write.frbsPMML The frbsPMML writer
DENFIS.eng DENFIS prediction function
HyFIS.update HyFIS updating function
frbs.learn The frbs model building function
No Results!

Last month downloads


License GPL (>= 2) | file LICENSE
Date 2013-02-26
NeedsCompilation no
Packaged 2015-05-22 10:01:26 UTC; Lala
Repository CRAN
Date/Publication 2015-05-22 13:19:10

Include our badge in your README